Recall

Captures content from diverse sources including web pages, videos, documents, emails, and meeting recordings through browser extensions, API integrations, and native connectors. Uses content extraction pipelines that normalize different media types into a unified internal representation, enabling downstream processing regardless of source format or platform.

Generates abstractive summaries of captured content using language models with configurable summarization depth (brief, detailed, key-points). The system maintains semantic coherence across different content types by applying type-specific summarization strategies (e.g., timeline extraction for videos, speaker identification for meetings) before applying unified abstractive summarization, preserving critical details while reducing verbosity.

Automatically detects and consolidates duplicate or near-duplicate content captured from multiple sources (e.g., same email forwarded multiple times, same meeting recording from different attendees). Uses fuzzy matching on content hashes and semantic similarity to identify duplicates, then merges them while preserving metadata from all sources (multiple timestamps, all attendees, etc.) to create a unified record.

Provides automated content lifecycle policies that move older or less-frequently-accessed content to cold storage, with configurable retention policies and archival rules. Implements tiered storage (hot/warm/cold) with different access latencies and costs, and supports selective restoration of archived content. Maintains searchability across all tiers while optimizing storage costs and performance.

Indexes all captured content using vector embeddings and enables semantic search queries that find relevant information even when exact keyword matches don't exist. The system maintains a searchable knowledge graph of ingested content with embeddings computed at multiple granularities (document-level, section-level, sentence-level) to support both broad and precise retrieval, using similarity-based ranking to surface contextually relevant results.

Automatically organizes captured content chronologically and reconstructs temporal relationships between items (e.g., linking emails to related meetings, connecting documents to their discussion context). The system extracts timestamps from all sources, normalizes them to a unified timeline, and builds temporal indices that enable browsing content by date ranges and discovering content clusters around specific time periods.

Analyzes user's current context (active document, meeting, email) and recommends relevant previously-captured content that may be useful. Uses content similarity, temporal proximity, and topic modeling to surface related information from the knowledge base, with ranking algorithms that prioritize recency, relevance, and user engagement patterns to surface the most contextually appropriate recommendations.

Enables sharing of captured content and summaries with team members through workspace collaboration features. Implements access control mechanisms (view-only, edit, admin permissions) and maintains audit trails of who accessed what content and when. Supports team-level content organization, commenting, and annotation workflows that allow multiple users to build shared knowledge bases while maintaining individual privacy boundaries.

Provides native integrations with popular productivity tools (Slack, Microsoft Teams, Google Workspace, Outlook) through API connectors and webhooks. Enables bidirectional data flow: capturing content from these platforms into Recall, and surfacing Recall summaries/recommendations back into user workflows through notifications, embeds, and slash commands, reducing context-switching friction.

Offers configurable processing pipeline where sensitive content can be processed locally on user's device before optional cloud processing for advanced features. Implements encryption-at-rest and in-transit for all captured content, with granular privacy controls allowing users to specify which content types or sources should remain local-only. Supports both fully local mode (for maximum privacy) and hybrid mode (local + cloud for enhanced features).

Automatically assigns tags and categories to captured content using NLP-based topic modeling and entity extraction. Learns from user's manual tagging patterns to improve automatic categorization over time, supporting both predefined category hierarchies and user-defined custom tags. Enables faceted search and filtering across multiple tag dimensions simultaneously, with conflict resolution when content matches multiple categories.

Enables exporting captured content and summaries to external knowledge management platforms (Notion, Obsidian, Roam Research, etc.) through API integrations and standardized formats (Markdown, JSON). Supports scheduled exports, selective export (by date range, tags, or search query), and bidirectional sync for platforms that support it, maintaining content relationships and metadata during export.